2016 National Youth Tobacco Survey

Methodology Report

For questions about this report, please email Sean Hu at shu@cdc.gov

Prepared for Centers for Disease Control and Prevention

Prepared by ICF, Rockville, Maryland

Centers for Disease Control and Prevention

Office on Smoking and Health

Atlanta, GA

December 15, 2016

Recommended Citation: Office on Smoking and Health. 2015 National Youth Tobacco Survey: 

Methodology Report. Atlanta, GA: U.S. Department of Health and Human Services, Centers for 

Disease Control and Prevention, National Center for Chronic Disease Prevention and Health 

Promotion, Office on Smoking and Health, 2015.

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Methodology Report: 2016 National Youth Tobacco Survey

Table of Contents • Page i

TABLE OF CONTENTS

CHAPTER 1—NYTS SAMPLING DESIGN ............................................................1

1.1 Overview of the National Youth Tobacco Survey (NYTS) ............................................... 1

1.2 Overview of the 2016 NYTS Methodology ...................................................................... 1

CHAPTER 2—NYTS SAMPLING METHODS .........................................................3

2.1 Overview ........................................................................................................................ 3

2.2 Stratification and Linking ................................................................................................ 7

2.3 Sampling Methods.........................................................................................................14

CHAPTER 3—NYTS DATA COLLECTION..........................................................18

3.1 Survey Instrument .........................................................................................................18

3.2 Recruitment Procedures................................................................................................18

3.3 Survey Administration....................................................................................................18

3.4 Participation Rates ........................................................................................................20

CHAPTER 4—WEIGHTING OF NYTS RESPONSE DATA .......................................21

4.1 Overview .......................................................................................................................21

4.2 Sampling Weights .........................................................................................................21

4.3 Nonresponse Adjustments.............................................................................................24

4.4 Post-stratification and Trimming ....................................................................................26

4.5 Analysis Strata and Variance Estimation .......................................................................30

APPENDICES

A. Questionnaire

B. Student Weight Detail

C. Common Core of Data Race/Ethnicity Definitions
Methodology Report: 2016 National Youth Tobacco Survey

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CHAPTER 1—NYTS SAMPLING DESIGN

1.1 Overview of the National Youth Tobacco Survey (NYTS)

In conjunction with the State Youth Tobacco Survey (YTS), the National Youth Tobacco Survey 

(NYTS) was developed to provide the data necessary to support the design, implementation, and 

evaluation of state and national tobacco prevention and control programs (TCPs).1,2In addition, 

NYTS data supplement other existing surveys, such as the Youth Risk Behavior Surveillance 

System (YRBSS), by providing more comprehensive data on tobacco-related indicators for both 

middle school (grades 6–8) and high school (grades 9–12) students. Tobacco-related indicators 

included in the NYTS are: tobacco use (e.g., bidis, cigarettes, cigars, tobacco pipes, smokeless 

tobacco, snus, dissolvable tobacco products, hookahs, and electronic cigarettes); exposure to 

secondhand smoke; smoking cessation; minors’ ability to purchase or obtain tobacco products; 

and, knowledge and attitudes about tobacco and familiarity with pro-tobacco and anti-tobacco 

media messages. NYTS data also serve as essential benchmarks against which TCPs can assess 

the extent of youth tobacco use. The NYTS provides multiple measures and data for six of the 20 

tobacco-related Healthy People 2020 objectives (USDHHS, 2010): TU-2, TU-3, TU-7, TU-11, 

TU-18 and TU-19.

First conducted during fall 1999 and again during the springs of 2000, 2002, 2004, 2006, and 2009, 

then annually starting in 2011, the NYTS provides data that are representative of all middle school 

and high school students in the 50 States and the District of Columbia. Beginning in 2011, the 

Centers for Disease Control and Prevention (CDC) and the Food and Drug Administration (FDA) 

have collaborated to administer the NYTS.

1.2 Overview of the 2016 NYTS Methodology

The 2016 NYTS employed a stratified, three-stage cluster sample design to produce a nationally 

representative sample of middle school and high school students in the United States. NonHispanic black students were oversampled. Sampling procedures were probabilistic and conducted 

without replacement at all stages and entailed selection of: 1) Primary Sampling Units (PSUs)

(defined as a county, or a group of small counties, or part of a very large county) within each

stratum; 2) Secondary Sampling Units (SSUs), (defined as schools or linked schools) within each 

selected PSU; and 3) students within each selected school. Participating students completed the 

survey via pencil and paper using a self-administered, scannable questionnaire booklet.

Participation in the NYTS was voluntary at both the school and student levels. At the student level, 

participation was anonymous. CDC’s Institutional Review Board (IRB) requires that parents be 

given the opportunity to opt their student out of participating in the survey. Schools used either 

passive or active permission forms at their discretion.

 

1 Centers for Disease Control and Prevention. (CDC) (2014). Best Practices for comprehensive tobacco control 

programs-2014. Atlanta, GA: US Department of Health and Human Services, Public Health Service, CDC.

2 Centers for Disease Control and Prevention. Surveillance and Evaluation Data Resources for Comprehensive 

Tobacco Control Programs. Atlanta, GA: Centers for Disease Control and Prevention, National Center for Chronic 

Disease Prevention and Health Promotion, Office on Smoking and Health; 2014.
Methodology Report: 2016 National Youth Tobacco Survey

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The final sample consisted of 248 schools, of which 202 participated, yielding a school 

participation rate of 81.5%. A total of 20,675 student questionnaires were completed out of a 

sample of 23,523 students, yielding a student participation rate of 87.9%. The overall participation 

rate, defined as the product of the school-level and student-level participation rates, was 71.6%. 

A weighting factor was applied to each student record to adjust for nonresponse and for varying 

probabilities of selection. Weights were adjusted to ensure that the weighted proportions of 

students in each grade matched national population proportions. 

The remainder of this report provides detailed information on the methodology used in the 2016

NYTS sample selection (Chapter 2), data collection (Chapter 3), and weighting of student response 

data (Chapter 4).
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CHAPTER 2—NYTS SAMPLING METHODS

2.1 Overview

The objective of the NYTS sampling design was to support estimation of tobacco-related 

knowledge, attitudes, and behaviors in a national population of public and private school students 

enrolled in grades 6 through 12 in the United States. More specifically, the study was designed to 

produce national estimates at a 95% confidence level by school level (middle school and high 

school), by grade (6, 7, 8, 9, 10, 11, and 12), by sex (male and female), and by race/ethnicity (nonHispanic white, non-Hispanic black, and Hispanic). Additional estimates also were supported for 

subgroups defined by grade, by sex, and by race/ethnicity, each within school level domains; 

however, precision levels varied considerably according to differences in subpopulation sizes.

The universe for the study consisted of all public and private school students enrolled in regular 

middle schools and high schools in grades 6 through 12 in the 50 U.S. States and the District of 

Columbia. Alternative schools, special education schools, Department of Defense operated 

schools, Bureau of Indian Affairs schools, vocational schools that serve only pull-out 

populations, and students enrolled in regular schools unable to complete the questionnaire 

without special assistance, were excluded. The NYTS employed a repeat cross-sectional design.

The 2016 NYTS is a continuation of the NYTS cycles that have taken place since 1999. The 

general sampling design framework used for the 2015 NYTS also was employed for the 2016

NYTS. 

2.1.1 Oversampling of Racial/Ethnic Minorities

To facilitate accurate prevalence estimates among racial/ethnic minority groups, prior cycles of 

the NYTS have employed multiple strategies to increase the number of non-Hispanic black and 

Hispanic students included in the sample. These approaches have included over-sampling PSUs in 

strata with a high proportion of racial/ethnic minority students, the use of a weighted measure of 

size (MOS), and double class selection in large schools that contained a sufficient proportion of 

minority students. The sampling design always seeks to balance increasing yields for minority 

students with overall precision, as oversampling leads to larger variances for overall estimates. As 

described below, the only oversampling that remained in the more efficient design of the 2016 

NYTS was double class sampling. This method has been shown to reduce design effects for survey 

estimates, which is defined as the variance of actual survey estimates divided by the variance of a 

simple random sample of the same size. It is a common useful measure of the precision of survey 

estimates.

A weighted measure of size (MOS) was previously used to increase the probability of selection of 

high racial/ethnic minority PSUs and schools using a Probability Proportional to Size (PPS) 

sampling design. The effectiveness of a weighted MOS in achieving oversampling is dependent 

upon the distributions of non-Hispanic black and Hispanic students in schools. The need for a 

weighted MOS is predicated on a relatively low prevalence of minority students in the population; 

however, this premise has become less tenable with the increase of nonwhite students in the 

population overall, specifically Hispanic students. The need for oversampling Hispanic students 

has been gradually reduced with the increasing numbers of Hispanics among the student 
Methodology Report: 2016 National Youth Tobacco Survey

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population. As seen below, some degree of oversampling non-Hispanic black students remains in 

the sampling design.

In 1990, the contactor, ICF (formerly Macro International Inc.), conducted the first in a series of 

simulation studies to investigate the impact of various weighting functions on the numbers and 

percentages of racial/ethnic minority students reached in Youth Risk Behavior Survey (YRBS).

3

Sampling strategies based on this work were incorporated into the NYTS, and these simulations 

have been updated with each cycle of the NYTS to ensure that the minimum amount of weighting 

in the MOS is being used, while still achieving adequate representation of non-Hispanic black and 

Hispanic students. When the possibility of using an unweighted measure of enrollment size was 

investigated for the 2012 NYTS, results demonstrated that adequate representation of nonHispanic black and Hispanic students could be achieved through the use of an unweighted MOS. 

Thus, starting with the 2013 NYTS and continuing for the 2014, 2015 and 2016 NYTS surveys, 

student enrollment was used as the unweighted MOS, leading to improvements in the statistical 

efficiency of the design.

The MOS used in the 2016 NYTS sampling design no longer oversampled schools with high 

minority concentrations directly. In addition, the allocation to strata was proportional so the 

oversampling approach via disproportional allocation also was no longer in effect. Nevertheless, 

double class selection was still implemented in the 2016 NYTS sampling.

In previous NYTS cycles, schools with high racial/ethnic populations were subject to double class 

selection. More specifically, two classes per grade were selected in these schools, compared to one 

class per grade in other schools, to increase the number of racial/ethnic minority students sampled. 

In the 2016 NYTS, double class selection was used only in large schools that had greater than 3%

non-Hispanic black student enrollment. The threshold was developed, and updated, to generate the 

necessary numbers of participating non-Hispanic black students to ensure estimation precision for 

this subgroup.

2.1.2 Frame Construction

The frame was constructed from separate sources obtained from the National Center for Education 

Statistics (NCES) and from a commercial vendor, Market Data Retrieval Inc. (MDR Inc.). The 

NCES files were the Common Core of Data (CCD) for public schools and Private School Survey 

(PSS) for private schools. 

The reason for moving to a frame built from multiple data sources was to increase the coverage of 

schools nationally. This dual-source frame build method was implemented for the 2014 NYTS 

survey for the first time4, and the method was replicated for the 2016 NYTS. Including schools 

sourced from the two NCES files resulted in a coverage increase among all public and non-public 

schools of 11.3%. 

 

3 Errecart, M. T. (1990, October 5). Issues in Sampling African-Americans and Hispanics in School-Based Surveys.

Atlanta, GA: Centers for Disease Control.

4 Redesigning National School Surveys: Coverage and Stratification Improvement using Multiple Datasets. William 

Robb, Kate Flint, Alice Roberts, Ronaldo Iachan - ICF International, FEDCASIC March 2014
Methodology Report: 2016 National Youth Tobacco Survey

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A cut-off in school size was added to ensure anonymity and the presence of all grades. Eligible 

schools needed an enrollment of at least 40 students across eligible grades. Exhibit 2-2 presents 

the number of schools and students in the frame by school level.

Exhibit 2-2: Number of Schools and Students by School Level in the School Frame

School Level Schools Students

High Schools 29,761 16,371,331

Middle Schools 47,217 12,238,295

2.1.3 Sampling Stages and Measure of Size

The three-stage cluster sample was stratified by racial/ethnic composition and urban versus rural

status at the first (primary) stage. PSUs were classified as “urban” if they are in one of the 54 

largest metropolitan statistical areas (MSAs) in the U.S using 2012 American Community Survey 

(ACS) data from the US Census Bureau. Otherwise, they were classified as “nonurban.”

Additionally, implicit stratification was imposed by geography by sorting the PSU frame by state 

and by 5-digit ZIP Code (within state). Within each stratum, a PSU was randomly sampled without 

replacement at the first stage.

In subsequent sampling stages, a probabilistic selection of schools and students was made from 

the sample PSUs. Unlike the 2014 NYTS, the 2016 NYTS is designed to balance the yields across 

grades. Therefore, the PSU subsampling is simplified to vary across school sizes but not between 

school level categories.

The sampling stages may be summarized as follows, with additional details provided in Section 2.2:

 Selection of PSUs: Eighty five PSUs were selected from 16 strata, with probability 

proportional to the total number of eligible students enrolled in all eligible schools located 

within a PSU.

 Selection of Schools: At the second sampling stage, a total of 170 large schools, or secondstage units (SSUs), were selected from the 85 sample PSUs. Two large schools were 

selected per sample PSU, one per level (middle or high), for a total of 170 large sample 

SSUs. An additional 20 medium SSUs and 30 small SSUs were selected from subsample 

PSUs, for a total of 220 sample SSUs (220= 170+30+20). Figure 2.1 shows the sample 

sizes for SSUs and the number of sampled schools. The PSU subsample was drawn as a 

simple random sample, and the schools were drawn with probability proportional to the 

total number of eligible students enrolled in a school.

 Selection of Students: Students were selected via whole classes, whereby all students enrolled 

in any one selected class were by default chosen for participation. Classes were selected from 

course schedules provided by each school that agreed to participate. Schedules were 

constructed such that all eligible students were represented one time only.
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Schools were stratified into large, medium, and small schools based on their ability to support two, 

one, or less than one class selection per grade. In large schools, an average of 1.46 classes were 

selected per grade by selecting two classes per grade in a subset of selected large schools and one 

class per grade in the remaining schools. The double class sampling took place in schools with nonHispanic black enrollments over the established 3% threshold. Specifically, double class sampling 

took place by design in 78 of the 170 sample large schools, or 45.8% of these schools.

The sampling approach utilized PPS sampling methods. In PPS sampling, when the MOS is 

defined as the count of final-stage sampling units, and a fixed number of units are selected in the 

final stage, the result is an equal probability of selection for all members of the universe. For the 

NYTS, we approximate these conditions, and thus obtain a roughly self-weighting sample.

The MOS also was used to compute stratum sizes and PSU sizes. Assigning an aggregate measure 

of size to PSU, the sample allocates the PSU sample in proportion to the student population. 

Exhibit 2-3 presents a high-level summary of the key sampling design features that will be 

described in detail in the next sections.
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EXHIBIT 2-3: KEY SAMPLING DESIGN FEATURES

2.2 Stratification and Linking

This section describes the following steps that are necessary for the selection of the first- and 

second-stage samples of PSUs and schools: organizing PSUs; linking schools into SSUs; and 

implementing the stratification and allocation methods at each of these stages.

2.2.1 Primary Sampling Unit (PSU)

Defining a PSU

In general, PSUs are geographic areas defined as counties or groupings of counties. In defining a 

PSU, several issues are considered:

1. Each PSU should be large enough to contain the requisite numbers of schools and students 

by grade, yet not so large as to be selected with near certainty.

2. Each PSU should be compact geographically so that field staff can go from school to school 

easily.

3. Recent data should be available to characterize each PSU.

4. Each PSU should contain at least four middle and five high schools.

Generally, counties were equivalent to PSUs with two exceptions: 

1. Low population counties were combined to provide sufficient numbers of schools and 

students; and 

2. Counties that were very large may have been split to avoid becoming certainty or nearcertainty PSUs. 

Certainty PSUs are those whose size is large enough to ensure selection with probability one (1.0) 

with a PPS sampling design that selects larger PSUs with larger probabilities. As certainty PSUs 

Sampling Units Stratification Measure of Size Designed Sample Size Actual Sample Size

1

Counties, 

portions of a 

county, or groups 

of counties

Urban vs. Nonurban

(2 strata);

Minority concentration

(8 strata)

Aggregate school 

size in target 

grades

85 Counties, portions of a 

county, or groups of 

counties

85 Counties, portions of 

a county, or groups of 

counties

2 Schools

Small, medium and large;

High school vs. middle 

school

Eligible enrollment

220 SSU (school) 

selections: 170 large 

schools, 20 medium 

schools and 30 small 

schools

220 SSUs; 248 physical 

schools; 202 participated

3

Classes / 

students

1 or 2 classes per grade (2 

per grade in large, highminority schools)

27,789 students sampled; 

21,397 participants

23,523 students 

sampled;

20,675 students 

participated
Methodology Report: 2016 National Youth Tobacco Survey

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lead to inefficiencies in the design, they were split so that the new smaller units were no longer 

selected with a probability of one. Near-certainty units also were split to build in a safety buffer in 

the PSU sizes. County population figures were aggregated from school enrollment data for the 

grades of interest.

The 2016 NYTS PSU definitions were based on the definitions developed in the coordinated 2013 

YRBS-NYTS cycle and also used in the 2014 and 2015 standalone NYTS cycles. The exact PSUs 

defined in the 2016 NYTS sampling frame were updated to ensure that all PSUs met the criteria 

above. The frame had 1,257 PSUs, 518 of which were comprised of one single county.

Stratification of PSUs

The PSUs were organized into 16 strata, based on urban/rural location (as defined above) and 

racial/ethnic minority enrollment of non-Hispanic blacks and Hispanics. In the traditional 

stratification used by the NYTS, the classification of PSUs into the two racial/ethnic minority 

strata, non-Hispanic black and Hispanic, is based on the predominant minority in the PSU. This 

classification is coupled with the density distribution of non-Hispanic blacks and Hispanics to 

subdivide each of the four primary strata into four substrata, indexed by 1-4 according to this 

density. The approach for computing stratum boundaries follows the cumulative square root of “f” 

method developed by Dalenius and Hodges.5 The boundaries or cutoffs change as the frequency 

distribution (“f”) for the racial groupings change from one survey cycle to the next. These rules 

are summarized below.

 If the PSU is within one of the 54 largest MSA in the U.S. it is classified as “urban,” 

otherwise it is classified as “nonurban.”

 If the percentage of Hispanic students in the PSU exceeded the percentage of non-Hispanic 

black students, then the PSU is classified as Hispanic. Otherwise it is classified as nonHispanic black.

 Hispanic urban and Hispanic nonurban PSUs were classified into four density groupings, 

depending upon the percentages of Hispanics in the PSU.

 For urban, high Hispanic PSU, the percentage cut points used to define the groups were 

26, 42, and 58%.

 For nonurban, high Hispanic PSU, the percentage cut points used to define the groups 

were 22, 48, and 66%.

 Non-Hispanic Black urban and non-Hispanic black nonurban PSUs also were classified 

into four groupings, depending upon the percentages of non-Hispanic blacks in the PSU. 

 For urban non-Hispanic black PSUs, the percentage cut points used to define the groups 

were 26, 40, and 56%.

 For nonurban High non-Hispanic black PSUs, the percentage cut points used to define 

the groups were 20, 34, and 56%.

 

5 Dalenius, T., & Hodges, J. L. (1959). Minimum Variance Stratification. Journal of American Statistical 

Association, 54, 88−101.
Methodology Report: 2016 National Youth Tobacco Survey

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Exhibit 2-4 presents the cutoffs used in defining the substrata by concentrations of black and 

Hispanic students in each of the four primary strata.

Exhibit 2-4: Cutoffs for Density Components of Strata

STRATUM Cutoff 1 Cutoff 2 Cutoff 3

BR 20% 34% 56%

BU 26% 40% 56%

HR 22% 48% 66%

HU 26% 42% 58%

Allocation of the PSU Sample

We designed and selected a sample of 85 PSUs that were allocated in proportion to student 

enrollment to maximize overall precision. We made adjustments to the initial allocation to ensure 

that racial/ethnic minority targets would be met. Specifically, the adjustments rounded fractional 

allocations, ensured that each stratum would have at least two sampled PSUs, and added balance 

to the distribution across strata. Exhibit 2-5 presents the allocation of the PSU sample to strata. 

EXHIBIT 2-5: STRATUM DEFINITION AND PSU ALLOCATION TO STRATA

Predominant 

Minority

Urban / NonUrban

Density 

Group 

Number

Stratum Student 

Population

Proportion of 

Student 

Population

Number of 

Sample 

PSU's

Non-Hispanic 

Black

Urban

1 BU1 2,358,013 0.082 7

2 BU2 1,359,656 0.048 4

3 BU3 731,764 0.026 2

4 BU4 357,741 0.013 2

Non-urban

1 BR1 2,785,585 0.097 9

2 BR2 1,544,563 0.054 5

3 BR3 1,111,390 0.039 3

4 BR4 415,282 0.015 2

Hispanic

Urban

1 HU1 3,517,692 0.123 10

2 HU2 2,659,047 0.093 8

3 HU3 1,901,104 0.066 6

4 HU4 2,015,886 0.071 5

Non-urban

1 HR1 4,848,313 0.170 14

2 HR2 1,353,998 0.047 3

3 HR3 990,358 0.035 3

4 HR4 659,232 0.023 2
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2.2.2 Schools

Linking into Second-stage Sampling Unit (SSU)

Schools were classified as “whole” for high 

schools if they have all high-school grades 9 

through 12, and whole for middle schools if they 

had all grades 6–8. Otherwise, they were 

considered a “fragment” school. Fragment 

schools were linked with other schools (fragment 

or whole) to form a linked school that has all 

grades present for a given level. This process is 

illustrated in Figure 2-2, where “Component 

School A” is linked with “Component School B,” to form a linked school. We linked schools 

before sampling using an algorithm developed for use in the national YRBS that links 

geographically proximate schools. Linked schools were treated as second-stage sampling units

(SSUs) with selection performed at the grade level, as described below.

Stratification

SSUs were stratified by school level (middle and high) and by size. Middle schools were those 

that contained any of grades 6 through 8, and high schools were those that contained any of grades 

9 through 12. Schools that contained a mix of high and middle school grades were split into two 

sampling units, or one for each level.

SSUs also were stratified by school size into small, medium, and large strata on the basis of their 

ability to support less than one, one, or two class selections per grade. Operationally, large SSUs 

contained at least 56 students at each grade level, medium SSUs contained between 28 and 55

students per grade, and small SSUs contained less than 28 students at any grade level.

2.2.3 Sample Sizes

This section provides the derivation of the NYTS sample sizes driven by target precision 

requirements overall and in key subgroups. The required student yields, or numbers of 

participating students, are translated into the necessary numbers of sample schools, and sample 

PSUs, using historical participation rates. 

The NYTS is designed to produce accurate estimation within a margin of error (MOE) of 5% at a 

95% precision level for the following key subgroup estimates:

 Middle and high school (school level): middle school students in total (grades 6–8 

combined) and high school students in total (grades 9–12 combined);

 Grade: individual grades 6, 7, 8, 9, 10, 11, and 12;

 Sex: males and females in total, by school level (male middle school students, female 

high school students), and by individual grade (6-grade males, 6-grade females);

 Race/Ethnicity: in total and by school level (e.g., Hispanic middle school students).

FIGURE 2-2: LINKED SCHOOL CONSTRUCTION AND 

GRADE SAMPLING FOR HIGH SCHOOLS

Fragment School A

(Whole)

Fragment School B

(Fragment)

Grade 9

Grade 10 Grade 10

Grade 11 Grade 11

Grade 12 Grade 12
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The sample sizes are developed to support analysis by individual grade and by sex without any 

special considerations in the sampling plan. Design effects were assumed by the design to be 

relatively small for subgroups that cut across schools; therefore, estimates by sex had better 

precision than other subgroups. Thus, the designed confidence intervals were ± 3%. Because the 

design is expected to yield a greater number of completed surveys from high school students than 

from middle school students, overall estimates are anticipated to be more precise at the high school 

level than those at the middle school level. Moreover, because within grade estimates by sex have 

slightly larger standard errors than those for estimates by grade alone, estimates of sex are expected 

within ± 5%.

The 2016 NYTS sampling design aimed at balancing student yields by grade, unlike the 2012 and 

2014 sample designs which aimed at balance by school level (middle and high school). Previous 

designs aimed at balance by school level had targets of 10,000 students per level. For the 2016 

NYTS, the target sample sizes correspond to approximately 3,000 participating students per grade

so they also ensure the precision of estimates by individual grade (e.g., sex by grade subgroup 

estimates on the basis of about 1,500 students).

Across the ten previous cycles of the NYTS, the school participation has averaged 86.5%, with a 

low of 75.4%. Student participation has averaged 90.5% with a low of 87.6%. Historical 

participation rates at both school and student levels, which guide the sampling design and sample 

sizes, are summarized in Exhibit 2-6. In calculating the sample sizes for the 2016 NYTS, we made 

our approach more robust by assuming a conservative combined rate (student x school) of 77%, 

which was slightly lower than the historical overall response rate from 1999-2014 of 77.9% (90.6% 

student x 85.9% school). The 2015 response rates were not yet available when the 2016 sampling 

plan was created. 

Schools were classified by size based on grade-level enrollments. This ensured that a sampled 

school of a given size classification was able to support the student sample sizes summarized in 

Exhibit 2-7 below.
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EXHIBIT 2-6: HISTORICAL SUMMARY OF NYTS PARTICIPATION RATES

YEAR School Participation Student Participation Overall

1999 90.3% 93.2% 84.2%

2000 90.0% 93.4% 84.1%

2002 83.1% 90.6% 75.3%

2004 92.7% 87.9% 81.5%

2006 91.6% 87.6% 80.2%

2009 92.3% 91.9% 84.8%

2011 83.2% 88.0% 73.2%

2012 80.3% 91.7% 73.6%

2013 75.4% 90.7% 68.4%

2014 80.2% 91.4% 73.3%

Average over all previous 

cycles 85.91% 90.64% 77.86%

The NYTS sample size calculations were based on the following assumptions:

 The main structure of the sampling design is consistent with the design used to draw the 

sample for prior cycles of the NYTS.

 The selection of a minimum of one SSU at the high school level and one SSU at the middle 

school level within each PSU. Some PSUs were selected to provide up to four extra schools. 

 SSUs with at least 56 students per grade were considered large, and those among the others 

with 28 students per grade were considered medium; otherwise, they were considered 

small.

 On average, each selected class includes 28 students (on the basis of historical averages).

 For SSUs classified as large, we sample double the amount of students in 46% of these 

schools, by sampling eight classes instead of four. 

 A 77% overall response rate (based on historical averages) calculated as the product of the 

school and student response rate.

Note that the double sampling in 46% of the large schools is implemented to achieve an average 

of 1.46 classes per large school. These numbers drive the establishment of the cutoff for double 

sampling in each cycle.

Based on these assumptions, 85 PSUs were selected at the first stage. Within each of 85 sample 

PSUs, two large schools were drawn, one at the middle school level to supply students in grades 

6 through 8, and one at the high school level to supply students in grades 9 through 12. In 

addition, 10 and 15 PSUs were independently sub-sampled to supply medium and small SSUs at 

each level, respectively. With the selection of one small middle school and one small high school 

 
Methodology Report: 2016 National Youth Tobacco Survey

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per subsample PSU, and similarly for medium schools, the sample included a total of 30 small 

schools and 20 medium schools.

The anticipated number of students selected from all sample schools was 27,789 students (before 

non-response). 

Exhibit 2-7 provides a detailed calculation of designed sample sizes across school level and school 

size categories.6 Section 3.4 compares these projections to the actual sample yields.

EXHIBIT 2-7: PLANNED SAMPLE SIZES FOR THE 2016 NYTS

PSU Size # of 

SSUs

Number of 

Schools 

Sampled

Number of 

Classes per 

School

Number 

of 

Students 

per Class

Number of 

Sampled 

Students Prior 

to Attrition

Combined 

School and 

Student 77% 

Response Rate

85

Large High 

School 85

Double 

classes: 39 8 28 8,758 6,744

Double 

classes: 46 4 28 5,141 3,958

Large 

Middle 

School

85

Double 

classes: 39 6 28 6,569 5,058

Double 

classes: 46 3 28 3,856 2,969

Large Total 170 24,324 18,729

10 

(subsample)

Medium 

High School 10 10 4 28 1,120 862

Medium 

Middle 

School

10 10 3 28 840 647

Medium 

Total 20 1,960 1,509

15 

(subsample)

Small High 

School 15 15 3.8 18.1 1,030 793

Small 

Middle 

School

15 15 2.8 11.2 475 366

Small 

Total 30 1,505 1,159

Overall 

Total 220 27,789* 21,397

*Note that this was anticipated number of students in all sampled schools, and the actual number of sampled 

students is derived only from participating schools (and is thus considerably lower). 

 

6

In this exhibit, the schools are secondary sampling units (SSUs), or “virtual schools”, created by combining actual, 

physical schools so that each virtual school unit has a complete set of grades for the level. The virtual schools are 

expanded to physical schools.
Methodology Report: 2016 National Youth Tobacco Survey

Page 14

2.2.3.1 Estimates by School Level and by Grade

Estimates by school level are required to support separate analysis of students across middle school 

grades (6, 7, and 8) and high school grades (9, 10, 11, and 12). However, schools tend to vary in 

their grade structures, an inconsistency that compromises the ability to easily and efficiently link 

schools for sampling purposes in a manner that also uniformly divides students by grade. For 

example, 9th grade students are served by both junior high schools with grades 7–9 and by high 

schools with grades 9–12. As a result, we have developed the school linking approach described in 

Section 2.2.2 that was applied independently for high schools and middle schools.

The exhibits in Section 3-4 present the student yields attained by school level overall and in the key 

minority subgroups (blacks and Hispanics), and show how the target sample sizes were exceeded in 

these subgroups in the 2016 NYTS. These were also substantially larger than the numbers attained 

in the 2015 NYTS.

The designed sample sizes are approximately balanced for school-level and for grade-level 

groupings. By achieving a minimum of 2,692 students per grade (see Exhibit 3-2), the sample 

ensured that estimates at the grade level achieve the required precision levels. 

2.2.3.3 Sex Group Estimates

The large sample size permitted analysis by sex without any special considerations in the sampling 

plan. During the class selection process, frames of eligible classes from co-educational schools in 

which classrooms were segregated by sex (i.e., an all-male or all-female class) were avoided, if 

possible.

2.2.3.4 Race/Ethnicity Group Estimates

In order to support separate analysis of the data for non-Hispanic white, non-Hispanic black and 

Hispanic students, in total and by school level, adequate sample sizes were required by the design 

for subgroups defined by: 1) school level by racial grouping; or 2) by sex grouping. Sample sizes 

were not designed, however, to support detailed analyses by sex and school level within 

racial/ethnic subgroups (e.g., middle school Hispanic males).

2.3 Sampling Methods

This section describes the methods used in the selection of PSUs, schools, grades, and classes of 

students. In this process, we define the probabilities of selection associated with the various 

sampling stages as follows:

 Probability of selecting PSUs;

 Probability of selecting schools;

 Probability of selection of grades;

 Probability of selecting classes and students.

These probabilities provide the basis for the sampling weights discussed in Chapter 4.
Methodology Report: 2016 National Youth Tobacco Survey

Page 15

The overall probability of selection for a student is the product of the probability of selection of 

the PSU, which contains a group of schools, multiplied by the conditional probability of selecting 

the student's school and the conditional probability of selecting the student's class. These steps are 

detailed in the selection below.

2.3.1 Primary Sampling Unit

Selection

Within each first-stage stratum, the PSUs were sorted by five-digit ZIP Code to attain a form of 

implicit geographic stratification. Implicit stratification, coupled with the PPS sampling method 

described below, ensured geographic sample representation. With PPS sampling, the selection 

probability for each PSU is proportional to the PSU’s measure of size.

The following systematic sampling procedures were applied to the stratified frame to select a PPS 

sample of PSUs. 

 Select 85 PSUs with a systematic random sampling method within each stratum. The 

method applies within each stratum a sampling interval computed as the sum of the 

measures of size for the PSUs in the stratum divided by the number of PSUs to be selected 

in the stratum.

 Subsample at random 10 of the sample PSUs for the medium school sample for each 

school level. 

 Subsample at random 15 of the sample PSUs for the small school sample for each school 

level. 

Although the revised sampling design is focused on balancing student yields by grade, rather than 

by school level, sampling is still done separately for middle schools and high schools.

Probability

If MOSklm is the measure of size for school k in PSU l in stratum m and if Km is the number of 

PSUs to be selected in stratum m, then Pp

lm is the probability of selection of PSU l in stratum m:

















MOS

MOS

P = K

.m

lm

m

P

lm

2.3.2 Schools

Selection

For large schools, one high school and one middle school were selected with PPS systematic 

sampling within a PSU. The schools were selected into the sample with probability proportional 

to the measure of size. (MOS). 

Small and medium schools were sampled independently from large schools; they were set in two 

separate strata sampled at lower rates. This approach was implemented by drawing subsample
Methodology Report: 2016 National Youth Tobacco Survey

Page 16

PSUs for small and medium school sampling as described earlier. One small school or medium 

school was then selected in each subsampled PSU with probability proportional to the MOS.

Replacement of Schools/School Systems

We did not replace refusing school districts, schools, classes, or students. We allowed for school 

and student nonresponse by inflating the sample sizes to account for nonresponse. With this 

approach, all schools were contacted in a coordinated recruitment effort, which is not possible for 

methods that allow for replacing schools.

Probability

The probability of selecting large school k in PSU l and stratum m, PLS

klm, at each level was 

computed as follows:

















MOS

MOS

P =

.lm

LS klm

klm

For medium schools, one school was drawn from each of 10 subsampled PSU at each level, so the 

probability of selection of a medium school then becomes,

 

















MOS

MOS

P =

.lm

MS klm

klm 10 / 85

For small schools, one school was drawn from each of 15 subsampled PSU at each level, so the 

probability of selection of a small school then becomes (the same for each level)

 

















MOS

MOS

P =

.lm

SS klm

klm 15/85

2.3.3 Grades

Selection

Except for linked schools, all eligible grades were included in the class selection for each school.

In linked schools, grades were selected independently. One component school was selected to 

provide classes at each grade level, and grades within component schools were drawn with 

probability proportional to grade enrollment.

Probability

Most SSUs in the sample contained one school. In these cases, all eligible grades were selected so 

that the probability of selecting a grade was 1.0.

In SSUs that were made up of multiple schools, the selection of each component school at each 

grade is made with PPS sampling. The school selections from each component school at each 

grade level were made independently.
Methodology Report: 2016 National Youth Tobacco Survey

Page 17

We denote this PG

jklm the probability of selecting grade j in SSU k, in PSU l, stratum m. For the j

th

grade within SSU k, this probability is equal to the ratio of the number of students at grade j in the 

component school to the total enrollment in grade j across all component schools within the SSU.

2.3.4 Classes

Selection

In large schools, an average of 1.46 classes per grade were selected by selecting 2 classes per grade 

in 46% of the selected large schools and one class per grade in the remaining large schools. The 

double class sampling took place in schools with greater than 3% non-Hispanic black enrollment 

and one class per grade in the remaining schools.

One class per grade was selected in medium schools. In small schools, that is, those that could not 

support a full class selection at each grade, all students in all eligible grades were taken into the 

sample.

All students in a selected class who could complete the survey without special assistance were 

considered eligible and offered the opportunity to participate in the survey. Refusing students were 

not replaced. Nonresponse at the student level was accounted for in the sample size using an 

average per class yield that assumed student response rates derived from historical experience with 

the NYTS.

A set of classes was identified for each school at each grade level such that every student in a given 

grade level was enrolled in exactly one of the classes in the set. For example, a required English 

course might be used. If the school’s estimated non-Hispanic black enrollment exceeded 3%, two 

classes were randomly selected, without replacement, from the list. Otherwise, one class was 

randomly selected. Selections were made at all eligible grade levels in the school.

Probability

The probability of selection of a class when there are Cjklm classes at grade j in school k, PSUi, 

stratum m is just 1/Cjklm or 2/Cjklm depending on whether one or two classes are taken in the school. 

All students in a selected class were chosen, so the probability of selection of a student is the same 

as the class (i.e., 1/Cjklm or 2/Cjklm). 

Note that the probability of student selection within a class does not vary by race, ethnicity, or sex. 

We denote this probability as PC

ijklm as the probability of selecting class i in grade j, school k, PSU 

l, stratum m. Since every student in a selected class is also selected, the probability of selecting 

any student in class i, grade j, school k, PSU l, stratum k, is also equal to PC

ijklm.
Methodology Report: 2016 National Youth Tobacco Survey

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CHAPTER 3—NYTS DATA COLLECTION

3.1 Survey Instrument

The NYTS collects data on key short-term, intermediate, and long-term tobacco prevention and 

control outcome indicators. The 2016 survey instrument included a total of 83 questions, with the 

first 5 collecting student demographic information and the remaining measuring a comprehensive 

set of tobacco-related topics (Appendix A). Specific areas covered by the survey included: 

prevalence of tobacco product use; knowledge of and attitudes toward tobacco use; pro- and antitobacco media and advertising; minors’ access to tobacco products; nicotine dependence; cessation 

attempts; exposure to second-hand smoke; harm perceptions; exposure to tobacco product 

warnings; and tobacco use prevention school curricula.

3.2 Recruitment Procedures

The schools selected to participate in the 2016 NYTS were located in 36 different states. 

Recruitment began in May of 2015 with calls to State Departments of Education and Health. 

Letters of support were obtained from various state agencies and used in mailings to districts and 

schools. A date for survey implementation was selected to optimize the efficiency of data 

collection while accommodating school schedules. In selecting a date, convenience to the school 

and its calendar were considered. Additionally, an effort was made to schedule groups of schools 

from the same school district or PSU around the same time to facilitate efficient travel to and 

survey implementation within selected schools. Recruiters used an electronic calendar on a secure 

shared drive to facilitate communication and to avoid scheduling two schools for the same data 

collector on the same day.

3.3 Survey Administration

Survey administration in the schools began on February 8, 2016, immediately after data collector 

training, and continued until June 7, 2016. Each data collector visited an average of three schools 

per week. While the details of each data collection varied, there were six core steps followed for 

every school: 1) pre-contact call with the principal or lead contact prior to arrival at the school; 2) 

entry meeting with the principal or lead contact; 3) entry meeting with teacher or group of teachers 

prior to survey administration; 4) survey administration; 5) post-survey meeting with the teacher 

or teachers; and 6) post-survey meeting with the principal or lead contact prior to leaving the 

school. Most survey administrations could be completed in 1 day, while at other times, due to the 

number of classes selected or alternating block schedules, the data collector needed to return for a 

second day. Procedures were designed to protect students’ privacy by assuring that student 

participation was anonymous and voluntary. Students completed a self-administered scannable 

questionnaire booklet via pencil and paper.

3.3.1 Data Collection Staffing

Data collectors were recruited from a pool of previously trained data collectors, as well as retired 

teachers associations, school health networks, and a variety of health education organizations. Data 

collector training was conducted on February 3–5, 2016.
Methodology Report: 2016 National Youth Tobacco Survey

Page 19

Key components of the training included the following:

 Pre-contact activities with the schools;

 Entry and exit meetings with school officials;

 Data collection protocols;

 Follow-up activities;

 Communication with headquarters staff.

3.3.2 Field Procedures

After schools had been recruited, classes selected, and a date scheduled, each school received a 

packet of pre-survey materials. These materials included all the information necessary to prepare 

the school for data collection. Teacher packets contained the parental permission forms that had to 

be given out to all students in the selected classes prior to data collection. The timing of these presurvey packet mailings was determined in part by the type of permission form being used by the 

school. Passive parental permission forms, or forms returned only if the parents do not want their 

child to participate, were sent approximately 1 week prior to the scheduled date of data collection 

in the majority of schools. Active parental permission forms, forms that must be returned with the 

parent’s signature in order for the child to participate, were sent out at least 2 weeks prior to the

scheduled date of data collection for schools that require active consent. Follow-up calls were 

made to the selected schools to answer any questions and to make sure materials were received 

and distributed to selected classes and students.

On a weekly basis, data collectors received mailings containing their assignments for the coming 

week, travel and logistics information, and their must-read weekly bulletin. Weekly bulletins 

underlined key performance issues, corrected misconceptions, provided consistent direction on 

any procedural changes, and kept everyone abreast of the latest must-have information. In addition 

to these mailings, boxes of survey supplies were sent to data collectors, either to the data collector’s 

home or hotel. These boxes contained all supplies necessary for completing the data collection, 

including questionnaires, data envelopes, field forms, and pencils. Data collectors were supplied 

with extra materials for emergency packs as well, which they carried with them at all times.

3.3.3 Classroom Selection

Students were selected for participation by default via the selection of whole classes (i.e., all 

students enrolled in a selected class were eligible to take the survey). The frames from which 

classes were chosen were constructed such that eligible students had one and only one chance of 

being selected. However, at times the specific method of selecting classes varied from school to 

school, according to how a school’s class schedule was structured. Typically, classes were selected 

from a list of required core courses such as English, social studies, math, or science. Among middle 

school students, and among high school students in a few States, physical education and/or health 

also were considered core courses. However, in a small number of schools, it was difficult to 

develop an appropriate frame using this approach. Therefore, in these schools, classes were 

selected by using a time of day (e.g., second period) when all eligible students were scheduled to 

be attending a class of one kind or another as the frame, and randomly selecting from all classes 
Methodology Report: 2016 National Youth Tobacco Survey

Page 20

held at this time. Lastly, in some schools, school homerooms were used as the frame for class 

selection. 

3.4 Participation Rates

Across the ten previous cycles of the NYTS, the school participation has averaged 86%, with a 

low of 75%. Student participation has averaged 91% with a low of 88%, and the overall response 

rate has averaged 80%. To be conservative, we assumed slightly lower values in developing the 

sample design for the 2016 NYTS: an assumed overall participation rate of 77%.

The actual response rates in 2016 differed from our projections. The 2016 NYTS survey attained 

an actual school participation rate of 81.5 % and a student participation rate of 87.9%. The overall 

participation rate, the product of the school-level and student-level participation rates was 71.6%.

While the participation rate is lower than the levels assumed in the projections, the shortfall has 

no meaningful impact on the estimation precision. As seen below, however, some design 

modifications may be recommended to increase the yields achieved for black students.

The 2016 NYTS data file contains responses from 20,675 students compared to the 21,397

responding students anticipated by the design. Exhibit 3-1 shows that student yields were slightly 

lower than projected numbers for non-Hispanic Blacks in middle school (but not in high school). 

Among Hispanics, yields far exceeded the projections at both levels. All numbers exceed the 

targets required for precision levels.

EXHIBIT 3-1: SAMPLE YIELDS FOR NON-HISPANIC BLACK AND HISPANIC STUDENTS BY SCHOOL LEVEL

Subgroup Projected Participants Actual Participants

Middle School non-Hispanic Blacks 1,775 1,738

Middle School Hispanics 1,775 2,755

High School non-Hispanic Blacks 1,975 2,179

High School Hispanics 1,975 3,172

Exhibit 3-2 shows number of respondents by grade. Again, while the numbers are lower than 

projected for a few grades (high school level), they exceed the target for precise subgroup 

estimation. 

EXHIBIT 3-2: SAMPLE YIELDS FOR STUDENTS BY GRADE

Subgroup Projected Participants Actual Participants

6

th Grade 3,057 3,239

7

th Grade 3,057 3,272

8

th Grade 3,057 3,192

9

th Grade 3,057 2,751

10th Grade 3,057 2,831

11th Grade 3,057 2,698

12th Grade 3,057 2,692
Methodology Report: 2016 National Youth Tobacco Survey

Page 21

CHAPTER 4—WEIGHTING OF NYTS RESPONSE DATA

4.1 Overview

This section describes the procedures used to weight the data collected in the 2016 NYTS. The 

process involved the steps outlined below:

 Sampling weights

 Nonresponse adjustments

 Weight trimming

 Post-stratification to national estimates of racial totals by grade, sex and school type

This section focuses on the development of the weights for the student response data. The final 

student level response data were weighted to reflect the initial probabilities of selection and 

nonresponse patterns, to mitigate large variations in sampling weights, and to post-stratify the data 

to known sampling frame characteristics.

4.2 Sampling Weights

The base weight is the inverse of the probability of selection for each responding student. The base

weight is adjusted to compensate for nonresponse, to alleviate excess weight variation, and to 

match the weighted data to known control totals. The base weight is computed by inverting the 

probabilities of selection at each stage to derive a stage weight. For each respondent, the stage 

weights are multiplied to form the overall sampling weight assigned to each student.

The NYTS computation of sampling weights begins at the student sampling stage, and then moves 

to the school and PSU sampling stages. This sequence allows the student sampling weights to 

incorporate adjustments for student nonresponse. These adjustments, described next, use 

enrollment data by sex and by grade collected for each participating school. Because the process 

begins with the student weights within a given grade, school and PSU, we refer to these weights 

as conditional weights. 

4.2.1 Adjusted Conditional Student Weights

The adjusted conditional student weight is the student weight given the selection of the PSU, 

school, and grade. This weight is the product of the inverse of the probability of selection and a 

nonresponse adjustment within weighting classes based on grade and sex. Note that this step also 

includes an approach designed to limit the nonresponse adjustment factor, an early step to avoid 

extreme weights and hence to control the variability in the weights.

This three-step process is simplified algebraically (see Appendix B) and computed directly as the 

ratio of the number of enrolled students to the number of responding students in a given weighting

class within a school. The weighting class definition is set dynamically so as to avoid extreme 

weights, as described next.

We denote the student selection weight WR

cklm, where the subscripts k, l, and m refer to the school, 

PSU and stratum as before. The subscript c refers to the weighting class, described below. This 
Methodology Report: 2016 National Youth Tobacco Survey

Page 22

weight is computed as below, where N is the number of enrolled students for each school (the 

counts are provided by the school during data collection by grade and sex) and R is the number of 

responding students in weighting class c within a given school:

R

N

W =

cklm

R cklm

cklm

The weighting class c is defined by a sequence of rules that depends on the number of responding 

students. This is done to avoid large weights for classes with low numbers of respondents. This 

process operates entirely within schools.

Initially, the weighting class is defined by grade and sex within each school. We then combine 

weighting classes if the weight for the class exceeds a maximum value, C. This cap C is computed 

using the following equation:

min( 10, )

2

N

N

C =

cklm

cklm

cklm

The combination sequence first groups males and females within grade. Both the cap and the 

weight then are recomputed. If the weight still exceeds the cap, grades are combined. The process 

is repeated, and if the student weight still exceeds the cap, the school is taken as the weighting

class.

This has the effect, within school, of setting an upper limit on the weight of 2 in weighting classes 

with an enrollment of less than 10, and 20% of the enrollment in weighting classes with an 

enrollment of more than 10. Note that the cap could be exceeded, however, in the rare cases where 

the weighting class is collapsed to the school level.

4.2.2 School Sampling Weights

For large schools, the partial school weight is the inverse of the probability of selection of the 

school given that the PSU was selected:

P

1

=

MOS

MOS

W =

LS

klm klm

LS .lm

klm 















For small schools, the partial school weight is:

P

1

=

MOS

MOS

W =

SS

klm klm

SS .lm

klm 















(85 /15)
Methodology Report: 2016 National Youth Tobacco Survey

Page 23

For medium schools, the partial school weight for both high schools and middle schools is:

P

1

=

MOS

MOS

W =

MS

klm klm

MS .lm

klm 















(85/10)

The overall weights for a given PSU, school and grade combination were the product of the 

adjusted PSU, school and grade-level weights.

4.2.3 Grade Sampling Weights

Grade selection occurs within linked schools where the grade is available in each of the linked 

schools, or school “components” that constitute the SSU. The partial weight for a grade, given the 

selection of the linked school containing it, is simply the inverse of the probability of selection 

described in Section 2.4. In a non-linked school, the weight is 1.0. We denote the grade weight as 

WG

jklm. 
Methodology Report: 2016 National Youth Tobacco Survey

Page 24

4.2.4 PSU Sampling Weights

The weight of the PSU is the inverse of the probability of selection of that PSU:

P

1

=

MOS

MOS

K

1

W =

P

lm lm

.m

m

P

lm 











. For small and medium school selections, the supporting sample PSUs were drawn as a subsample.

(Subsampling of PSUs also was conducted for the large high schools.) This PSU subsampling 

component of the PSU weight is accounted for in the school selection probability and 

corresponding weight.

4.2.5 Overall Sampling Weight

The overall sampling weight is formed as the product of the stage selection weights. This weight, 

WT1, is then adjusted for nonresponse, trimmed, and poststratified to control totals, as described in 

the following sections. This weight is computed as:











W =W W W W

W =W W W W

W =W W W W

R

hijklm

G

jklm

SS

klm

P

lm

T1

hijklm

R

hijklm

G

jklm

MS

klm

P

lm

T1

hijklm

R

hijklm

G

jklm

LS

klm

P

lm

T1

hijklm

for large, medium and small schools, respectively, where the weights in the latter portions of the 

equations are defined in the preceding sections.

4.3 Nonresponse Adjustments

Nonresponse adjustment of weights is important to reduce potential bias incorporated into surveys 

from differences between responding and nonresponding schools included in the sample. In the 

2016 NYTS cycle, the nonresponse adjustment methods were refined to further minimize 

nonresponse bias potential. As opposed to the previous method, which created adjustment cells 

based on sampling strata, the new method defines nonresponse adjustment cells in a more tailored 

and systematic approach stemming the from non-response analysis. Specifically, the definition of 

the most appropriate nonresponse adjustment weighting cells followed these steps:

1. Conduct bivariate analysis to identify key predictors of school non-response and student 

non-response; 

2. Conduct multivariate logistic regression analysis, or response propensity models, including 

the subset of key predictors identified in #1 to identify significant predictors of nonresponse at both levels; 

3. Develop non-response adjustment weighting cells based on the significant predictors while 

incorporating information about correlations between predictors and resulting cell sizes.
Methodology Report: 2016 National Youth Tobacco Survey

Page 25

Variables identified in the non-response bivariate and multivariate analyses included the 

following:

 Urban status;

 School type (public/private);

 A range of socio-economic status (SES) indicators and concentrations of minority 

students, all of which are mutually correlated.

These analyses are detailed in the nonresponse analysis report. The subset of variables selected for 

defining weight adjustment cells is effectively reduced by eliminating variables with high pairwise 

correlations and limiting to variables with adequate representation of responding schools. As a 

result, the variables used in creating nonresponse adjustment cells were school type and perstudent Title I spending. Because of the small number of private schools included in the sample, 

they were included as their own category in the nonresponse adjustment cells. Nonresponse 

adjustment was conducted separately for middle schools and high schools.

The adjustment process uses the following equations for the adjustment factor:

Am =

∑ (Wlm

P

k,l∈schools sampled ∗ Wklm ∗ MOSklm)

∑ (Wlm

P

k,l∈schools with respondents ∗ Wklm ∗ MOSklm)

The student weight adjusted for nonresponse is then:

W3

s = W2s

∗ Am

The school response rates by weighting cells along with resulting nonresponse adjustments are 

presented in Exhibit 4-1. 

EXHIBIT 4-1.ADJUSTMENT CELLS

Stratum (Non-Response) Sampled 

Schools

Responding 

Schools

Percent 

Responding

NonResponse 

Adjustment

Middle School

 Private 9 7 77.78% 1.1

 Public/Low Per-Student Title I Spending 30 26 86.67% 1.2

 Public/High Per-Student Title I Spending 88 81 92.05% 1.1

 Total 127 114 89.76% .

High School

 Private 13 6 46.15% 1.7

 Public/ Low Per-Student Title I Spending 33 22 66.67% 1.5

 Public/ High Per-Student Title I Spending 74 59 79.73% 1.2

 Total 120 87 72.50% .

 Overall Total 247 201 81.38% .
Methodology Report: 2016 National Youth Tobacco Survey

Page 26

For the 2016 NYTS, a total of 46 schools did not respond. The final analytic file contains student 

responses from schools in 82 PSUs, with approximately 81.5% of schools responding overall 

(89.8% for middle schools and 72.5% for high schools). The largest adjustment was for private 

high schools where only 6 of 13 responded respectively. The resulting nonresponse adjustment 

factor was 1.7.

4.4 Post-stratification and Trimming

The final two steps in the weighting process include trimming and post-stratification. Trimming 

procedures are used to control the weight variability and reduce its impact on survey variances. 

Post-stratification methods ensure that weighted totals sum to population control totals and 

therefore minimize the potential for biases due to non-response and non-coverage.

In previous NYTS cycles, extreme weights were first trimmed before being post-stratified to match 

public and private school enrollment counts. When trimming is performed prior to poststratification it can be less effective at controlling weight variability added back by poststratification step. If trimming follows post-stratification, on the other hand, then final weights 

would not sum to known population control totals, and the bias reduction benefits of poststratification would be diluted.

The solution first used in the 2015 NYTS is an iterative approach that combines post-stratification 

and trimming.7,8 The methods incorporate a model-based approach to variable selection in weight 

trimming while controlling for extreme variability in weights across sampling units. Iterative poststratification, or raking, approaches have long been used in weighting survey data to allow 

additional post-stratification variables and categories.9,10 By combining the two iterative methods 

in one approach, the rake-trim method ensures that trimmed weights retain their variance-reducing 

feature after post-stratification. Conversely, it also ensures that post-stratified weights add up to 

control totals. 

Similar to weighting, the raking and trimming methods were conducted separately for middle 

schools and high schools. In each iteration of the raking method used in the 2016 NYTS, poststratification is performed along two dimensions: a) school type (public or private)/ grade/ raceethnicity, and b) school type/ grade/ gender. These two classes are defined so that control totals 

are known and cells have reasonable size. Public schools are raked to grade and race-ethnicity 

marginal totals while private schools are raked to grade totals. Within the same iteration, this step 

is followed by the trimming step which truncates (or “caps”) the weight using the overall weight 

distribution (i.e. percentiles). The trimming method uses the interquartile range (IQR) as the basis 

for a threshold for weights that are excessively large. Specifically, any weights that exceed the 

 

7

Iachan R (2010, August). A new iterative method for weight trimming and raking. Paper presented at the American Statistical 

Association meeting. Vancouver. Canada

8

Izrael D, Battaglia MP, Frankel MR. (2009). Extreme survey weight adjustment as a component of sample balancing (a.k.a. 

Raking), Paper 274-2009, SAS Global Forum 2009.

9 Potter F. (1998). Survey of procedures to control extreme sampling weights. ASA Proceedings of the Section on Survey 

Research Methods, 446-457.

10 Oh HL, and Scheuren F. (1978). Some Unresolved Application Issues in Raking Ratio Estimation.

1978 Proceedings of the Section on Survey Research Methods, Washington, DC: American

Statistical Association, pp. 723-728.
Methodology Report: 2016 National Youth Tobacco Survey

Page 27

median weight plus 4 times the IQR are trimmed.3 The excess weight is then distributed among 

the observations within each cell to ensure that effective post-stratification totals are preserved. 

National estimates of race-ethnicity and gender percentages by school type and grade were 

obtained from two sources: 

 Private school enrollments by grade and five racial/ethnic groups were obtained from the 

Private School Universe Survey (PSS) School Year 2011-12, the most recent PSS dataset 

available;

 Public school enrollments by grade, sex, and five racial/ethnic categories were obtained 

from the Common Core of Data (CCD) Public Elementary/Secondary School Universe 

Survey: School Year 2013-14, the most recent CCD data file available (See Appendix C).

Both databases are produced by the National Center for Education Statistics (NCES). Raw schoollevel data files were downloaded and processed to mirror eligibility requirements imposed on the 

sampling frame. Specifically, eligibility was defined in terms of a school type variable present on 

both files. Exhibit 4-2 provides the details of this categorization. In both cases, eligible schools 

were defined as “regular” schools—that is, those schools left after schools that either serve special 

populations or pull students from other eligible schools are removed. Furthermore, public schools 

were limited to those that had not closed since the time of the last CCD survey.

EXHIBIT 4-2. ELIGIBILITY CRITERIA FOR CONTROL TOTALS

Value of 

School Type Public School (CCD) Coding Private School (PSS) Coding Eligibility Status

1 Regular Regular Eligible

2 Special Education Montessori Not Eligible

3 Vocational Special Program Emphasis Not Eligible

4 Alternative/Other Special Education Not Eligible

5 -unused- Career / Technical / Vocational Not Eligible

6 -unused- Alternative / Other Not Eligible

7 -unused- Early Childhood Not Eligible

The private school and public school databases were then combined to produce the enrollments 

for all schools and to develop population percentages to use as controls in the post-stratification 

step. Exhibit 4-3 gives counts of schools and students by grade for private and public schools by 

school eligibility criteria.
Methodology Report: 2016 National Youth Tobacco Survey

Page 28

EXHIBIT 4-3. COUNTS OF SCHOOLS AND STUDENTS BY SCHOOL TYPE AND ELIGIBILITY STATUS

Type / Grade

Ineligible Counts Eligible Counts Total (Raw File)

Schools Students Schools Students Schools Students

Public 6 3,184 26,652 34,465 3,684,600 37,649 3,711,252

7 3,808 32,796 27,466 3,744,770 31,274 3,777,566

8 4,036 41,269 27,526 3,740,848 31,562 3,782,117

9 6,231 112,704 20,583 3,893,260 26,814 4,005,964

10 6,483 133,158 19,715 3,651,668 26,198 3,784,826

11 6,516 156,904 19,568 3,389,928 26,084 3,546,832

12 6,502 216,613 19,454 3,272,177 25,956 3,488,790

Total 36,760 720,096 168,777 25,377,251 205,537 26,097,347

Private 6 2,027 16,916 14,598 290,654 16,625 307,570

7 1,918 16,343 13,922 285,467 15,840 301,810

8 2,007 17,510 13,694 283,706 15,701 301,216

9 1,687 18,519 6,580 265,350 8,267 283,869

10 1,636 19,012 6,279 260,393 7,915 279,405

11 1,606 18,822 6,068 255,640 7,674 274,462

12 1,552 19,476 5,916 252,489 7,468 271,965

Total 12,433 126,598 67,057 1,893,699 79,490 2,020,297

Total 6 5,211 43,568 49,063 3,975,254 54,274 4,018,822

7 5,726 49,139 41,388 4,030,237 47,114 4,079,376

8 6,043 58,779 41,220 4,024,554 47,263 4,083,333

9 7,918 131,223 27,163 4,158,610 35,081 4,289,833

10 8,119 152,170 25,994 3,912,061 34,113 4,064,231

11 8,122 175,726 25,636 3,645,568 33,758 3,821,294

12 8,054 236,089 25,370 3,524,666 33,424 3,760,755

Total 49,193 846,694 235,834 27,270,950 285,027 28,117,644

Overall, the total number of eligible students is 52,564,412. This total matches the sum of the 

adjusted weights prior to scaling. The control totals used in even and odd iterations of poststratification are included below in Exhibits 4-4a and 4-4b.
Methodology Report: 2016 National Youth Tobacco Survey

Page 29

EXHIBIT 4-4A. CONTROL TOTALS USED IN EVEN ITERATIONS OF THE RAKING AND TRIMMING METHOD

School Type Grade Race/Hispanic Origin Control 

Total

Private 6 Combined 290,654

Private 7 Combined 285,467

Private 8 Combined 283,706

Private 9 Combined 265,350

Private 10 Combined 260,393

Private 11 Combined 255,640

Private 12 Combined 252,489

Public 6 Non-Hispanic Native American 39,520

Public 6 Non-Hispanic Asian and Pacific Islander 184,901

Public 6 Non-Hispanic Black 583,490

Public 6 Hispanic 964,837

Public 6 Non-Hispanic White 1,911,852

Public 7 Non-Hispanic Native American 40,144

Public 7 Non-Hispanic Asian and Pacific Islander 184,802

Public 7 Non-Hispanic Black 595,383

Public 7 Hispanic 968,060

Public 7 Non-Hispanic White 1,956,381

Public 8 Non-Hispanic Native American 40,378

Public 8 Non-Hispanic Asian and Pacific Islander 187,423

Public 8 Non-Hispanic Black 596,542

Public 8 Hispanic 943,769

Public 8 Non-Hispanic White 1,972,736

Public 9 Non-Hispanic Native American 43,308

Public 9 Non-Hispanic Asian and Pacific Islander 181,738

Public 9 Non-Hispanic Black 653,529

Public 9 Hispanic 981,991

Public 9 Non-Hispanic White 2,032,694

Public 10 Non-Hispanic Native American 39,499

Public 10 Non-Hispanic Asian and Pacific Islander 182,627

Public 10 Non-Hispanic Black 581,746

Public 10 Hispanic 884,309

Public 10 Non-Hispanic White 1,963,487

Public 11 Non-Hispanic Native American 34,996

Public 11 Non-Hispanic Asian and Pacific Islander 182,576

Public 11 Non-Hispanic Black 514,866

Public 11 Hispanic 784,371

Public 11 Non-Hispanic White 1,873,119

Public 12 Non-Hispanic Native American 33,759

Public 12 Non-Hispanic Asian and Pacific Islander 180,239

Public 12 Non-Hispanic Black 483,886

Public 12 Hispanic 728,270

Public 12 Non-Hispanic White 1,846,023
Methodology Report: 2016 National Youth Tobacco Survey

Page 30

EXHIBIT 4-4B. CONTROL TOTALS USED IN ODD ITERATIONS OF THE RAKING AND TRIMMING METHOD

School Type Grade Gender Control Total

Private 6 Combined 290,654

Private 7 Combined 285,467

Private 8 Combined 283,706

Private 9 Combined 265,350

Private 10 Combined 260,393

Private 11 Combined 255,640

Private 12 Combined 252,489

Public 6 Male 1,887,892

Public 6 Female 1,796,756

Public 7 Male 1,918,000

Public 7 Female 1,826,425

Public 8 Male 1,912,185

Public 8 Female 1,827,861

Public 9 Male 2,008,655

Public 9 Female 1,885,266

Public 10 Male 1,861,965

Public 10 Female 1,789,926

Public 11 Male 1,711,004

Public 11 Female 1,678,377

Public 12 Male 1,642,782

Public 12 Female 1,628,267

4.5 Analysis Strata and Variance Estimation

Sampling variances for complex sampling designs can be estimated using one of several methods, 

including linearized estimators and balanced repeated replication. These methods are implemented 

with a variety of software packages, including SUDAAN, WesVar, Stata and SAS using special 

sample survey procedures (such as Proc SurveyMeans in SAS Version 9). The 2016 NYTS data 

were prepared for estimating variances using the linearization method.

Because estimates are typically reported separately for middle schools and high schools, analysis 

strata need to ensure that each stratum has two or more PSUs for variance estimation within each 

subpopulation (middle schools and high schools separately). 

As noted earlier, the allocation ensured that every stratum had at least two PSUs in the sample. 

This does not necessarily translate to two PSUs with valid student data for each school level 

(middle schools and high schools) in every stratum due to the effects of nonresponse at the school 

level. In particular, nonparticipating schools may lead to PSUs without student data for a given 

school level.

Exhibit 4-6 displays the correspondence between the sampling strata and the analysis strata, which 

are represented by two variables on the analysis file. As analyses are typically conducted separately 

for middle and high schools, we ensure that each stratum is represented in the data file with at least 

two PSUs for each school level separately. All strata/level combinations but four had at least two 
Methodology Report: 2016 National Youth Tobacco Survey

Page 31

PSUs, two combinations for middle schools and two for high school. As a result, two strata were 

collapsed for middle and high schools, as shown in this exhibit. Thus, the analytic file contains 14

values in the analysis strata variable for middle and high schools. In addition, stratum codes used 

in sampling and weighting were converted to a numeric “analysis stratum” code for use in 

SUDAAN, which requires numeric variables.

Exhibit 4-7 presents selected key survey estimates and their sampling errors estimated using Taylor 

series linearization method, which is usually employed by NYTS data analysts, and implemented with 

SUDAAN or similar software (e.g., SAS Proc SurveyMeans). Specifically, the Exhibit presents the 

percent and standard error of the percent for estimates of current use of selected tobacco products 

separately for high schools (Exhibit 4-7A) and middle schools (Exhibit 4-7B).

EXHIBIT 4-6. SAMPLING AND ANALYSIS STRATUM CODING SCHEMES

HSMS Design 

Stratum

Design 

Stratum 

Count

Analysis 

Stratum

Analysis 

Stratum 

Count

HS BR1 6 BR1 6

HS BR2 5 BR2 5

HS BR3 2 BR3 2

HS BR4 2 BR4 2

HS BU1 5 BU1 5

HS BU2 3 BU2 3

HS BU3 1

BU3+BU4 3

HS BU4 2

HS HR1 13 HR1 13

HS HR2 3 HR2 3

HS HR3 3

HR3+HR4 4

HS HR4 1

HS HU1 7 HU1 7

HS HU2 6 HU2 6

HS HU3 5 HU3 5

HS HU4 5 HU4 5

MS BR1 9 BR1 9

MS BR2 4 BR2 4

MS BR3 3 BR3 3

MS BR4 2 BR4 2

MS BU1 5 BU1 5
Methodology Report: 2016 National Youth Tobacco Survey

Page 32

EXHIBIT 4-6. SAMPLING AND ANALYSIS STRATUM CODING SCHEMES (CONTINUED)

HSMS Design 

Stratum

Design 

Stratum 

Count

Analysis 

Stratum

Analysis 

Stratum 

Count

MS BU2 4 BU2 4

MS BU3 2 BU3 2

MS BU4 2 BU4 2

MS HR1 14 HR1 14

MS HR2 3 HR2 3

MS HR3 2 HR3 2

MS HR4 2 HR4 2

MS HU1 8 HU1 8

MS HU2 8 HU2 8

MS HU3 5 HU3 5

MS HU4 5 HU4 5

EXHIBIT 4-7A: CURRENT USE ESTIMATES FOR SELECTED TOBACCO PRODUCTS FOR HIGH SCHOOL STUDENTS

Product Overall % (SE) Female% (SE) Male% (SE) White% (SE) Black% (SE) Hispanic% (SE)

CBIDIS 0.58% (0.09%) 0.42% (0.11%) 0.74% (0.13%) 0.40% (0.11%) 0.35% (0.13%) 0.98% (0.25%)

CCIGAR 7.82% (0.56%) 5.65% (0.68%) 10.01% (0.65%) 7.75% (0.74%) 9.82% (0.87%) 7.67% (0.86%)

CCIGT 8.18% (0.66%) 7.02% (0.77%) 9.31% (0.80%) 9.83% (0.85%) 4.66% (0.64%) 7.10% (0.85%)

CDISSOLV 0.97% (0.14%) 0.94% (0.22%) 1.02% (0.15%) 0.86% (0.19%) 0.45% (0.12%) 1.46% (0.24%)

CELCIGT 11.38% (0.76%) 9.53% (0.90%) 13.21% (0.90%) 13.74% (0.96%) 6.43% (0.73%) 10.65% (1.17%)

CHOOKAH 4.97% (0.41%) 5.20% (0.53%) 4.69% (0.40%) 4.43% (0.43%) 4.10% (0.47%) 6.93% (0.92%)

CPIPE 1.49% (0.14%) 1.02% (0.14%) 1.97% (0.24%) 1.42% (0.15%) 1.14% (0.29%) 1.80% (0.28%)

CROLLCIGTS 3.60% (0.49%) 2.99% (0.54%) 4.18% (0.61%) 3.44% (0.64%) 2.98% (0.51%) 4.27% (0.58%)

CSLT 4.42% (0.39%) 1.58% (0.26%) 7.30% (0.70%) 5.81% (0.55%) 1.83% (0.35%) 3.37% (0.54%)

CSNUS 2.30% (0.24%) 1.84% (0.33%) 2.76% (0.32%) 2.82% (0.33%) 0.74% (0.21%) 2.09% (0.36%)

EXHIBIT 4-7B: CURRENT USE ESTIMATES FOR SELECTED TOBACCO PRODUCTS FOR MIDDLE SCHOOL STUDENTS

Product Overall% (SE) Female% (SE) Male% (SE) White% (SE) Black% (SE) Hispanic% (SE)

CBIDIS 0.29% (0.06%) 0.23% (0.08%) 0.35% (0.10%) 0.16% (0.07%) 0.36% (0.23%) 0.49% (0.13%)

CCIGAR 2.11% (0.31%) 1.61% (0.30%) 2.58% (0.52%) 1.33% (0.24%) 4.49% (0.97%) 2.48% (0.56%)
Methodology Report: 2016 National Youth Tobacco Survey

Page 33

EXHIBIT 4-7B: CURRENT USE ESTIMATES FOR SELECTED TOBACCO PRODUCTS FOR MIDDLE SCHOOL STUDENTS

(CONTINUED)

Product Overall% (SE) Female% (SE) Male% (SE) White% (SE) Black% (SE) Hispanic% (SE)

CCIGT 2.05% (0.26%) 1.72% (0.31%) 2.35% (0.38%) 2.03% (0.30%) 1.73% (0.51%) 2.03% (0.38%)

CDISSOLV 0.37% (0.08%) 0.21% (0.13%) 0.50% (0.13%) 0.36% (0.11%) 0.17% (0.08%) 0.48% (0.21%)

CELCIGT 4.23% (0.33%) 3.44% (0.41%) 5.01% (0.47%) 3.66% (0.41%) 4.72% (1.01%) 5.50% (0.76%)

CHOOKAH 1.91% (0.23%) 1.89% (0.26%) 1.95% (0.35%) 0.91% (0.17%) 2.75% (0.74%) 3.40% (0.43%)

CPIPE 0.58% (0.11%) 0.48% (0.13%) 0.69% (0.18%) 0.27% (0.09%) 0.59% (0.29%) 1.22% (0.32%)

CROLLCIGTS 1.41% (0.21%) 0.93% (0.19%) 1.88% (0.29%) 1.15% (0.27%) 1.58% (0.48%) 1.85% (0.29%)

CSLT 1.62% (0.31%) 0.90% (0.31%) 2.32% (0.41%) 1.73% (0.31%) 0.50% (0.21%) 1.91% (0.43%)

CSNUS 0.84% (0.16%) 0.47% (0.12%) 1.20% (0.26%) 0.98% (0.23%) 0.36% (0.17%) 1.01% (0.30%)

Exhibit 4-8 provides example specifications for applying the method with both SAS and SUDAAN 

for computing weighted prevalence estimates and their estimated variances.

EXHIBIT 4-8: EXAMPLE: ESTIMATES, CURRENT USE BY SCHOOL TYPE

SAS:

SAS:

Proc Surveymeans Data=nyts2016 mean;

Var ccigt_r ccigar_r cslt_r chookah_r celcigt_r;

Class ccigt_r ccigar_r cslt_r chookah_r celcigt_r;

Stratum stratum2;

Cluster psu2;

Weight wt;

Domain HSMS HSMS*Sex HSMS*Race_S;

Title “NYTS 2016, Estimates by School Type, by School Type and Sex Cross-Classified, and by School Type and 

Race/Ethnicity Cross-Classified”;

run;

SUDAAN:

Proc Descript Data=nyts2016 Filetype= SAS Design=WR;

Var ccigt_r ccigar_r cslt_r chookah_r celcigt_r;

Catlevel 1 1 1 1 1;

Nest Stratum2 PSU2 / Missunit;

Weight wt;

Subgroup HSMS Sex Race_S;

Levels 2 2 3;

Tables HSMS HSMS*Sex HSMS*Race_S;

Title “NYTS 2016, Estimates by School Type, by School Type and Sex Cross-Classified, and by School Type and 

Race Cross-Classified”;

Print Percent Sepercent / Style=NCHS;

run;
Appendix A

Questionnaire
Methodology Report: 2016 National Youth Tobacco Survey

Appendix A • Page 1

APPENDIX A. QUESTIONNAIRE

Questionnaire only included in PDF version of this 

document. 
Appendix B

Student Weight Detail
Methodology Report: 2016 National Youth Tobacco Survey

Appendix B • Page 1

APPENDIX B. STUDENT WEIGHT DETAIL

Students are selected from schools via the selection of intact class sections as described in Section 

2.4.4. The student sampling weight is computed based on a ratio of enrolling to responding 

students described in Section 4.2.1. The purpose of this section is to show that the resulting student 

weight is equivalent to computing a student weight as the inverse of the selection probability—are 

the other stage sampling weights—followed by two adjustments, one for nonresponse, and another 

poststratifying to known enrollment totals.

For the purposes of clarity, we omit the subscripts denoting the sampling stages and weight class. 

The unsubscripted quantities presented are assumed to be within weight class c, as defined in 

section 4.2.1.

The probability of selection of a class when there are Cjklm classes at grade j in school k, PSUi, 

stratum m is just 1/Cjklm or 2/Cjklm, depending on whether 1 or 2 classes are taken in the school. All 

students in a selected class were chosen so the probability of selection of a student is the same as 

the class, as well as constant across students within student weighting class. The initial selection 

probability is taken to be the inverse of this sampling probability.

In our simplified notation, letting K represent the number of sampled class sections, we have:

K

C W 

Nonresponse Adjustment

The nonresponse adjustment inflates the weight of the responding students to equal that of the 

sampled students. The adjustment is calculated as the sum of the weights for sampled students to 

the sum of the weights for responding students;

R

n

F  





Responding

Selected

NR W

W

where n represents the number of sampled students and R represents the number of responding 

students in the student weight class. Note that the equation simplifies to a ratio that does not involve 

W, as W is constant within the class.

Enrollment Ratio Adjustment

Next, the nonresponse adjusted student weights are ratio adjusted to conform to known school 

enrollment totals for each grade and sex. The adjustment Fps is computed as

R W

N

W

N

F

 









ps
Methodology Report: 2016 National Youth Tobacco Survey

Appendix B • Page 2

where N is the number of enrolled students in the weight class, and 

W W FNR



 

The fully adjusted student weight is computed as:

W W FPS







Simplifying, we get

R

N

R W

N

W

W W FPS



 

 

  
Appendix C

Common Core of Data 

Race/Ethnicity Definitions
Methodology Report: 2016 National Youth Tobacco Survey

Appendix C • Page 1

APPENDIX C. COMMON CORE OF DATA RACE/ETHNICITY DEFINITIONS

Non-Hispanic American Indian/Alaska Native—A person having origins in any of the original 

peoples of North and South America (including Central America) and who maintains cultural 

identification through tribal affiliation or community recognition.

Non-Hispanic Asian/Pacific Islander—A person having origins in any of the original peoples of 

the Far East, Southeast Asia, the Indian subcontinent, or the Pacific Islands. This area includes, for 

example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, Thailand, Vietnam, Guam, 

the Philippine Islands, Samoa, and other Pacific Islands.

Non-Hispanic Black—A person having origins in any of the black racial groups of Africa; African 

American.

Hispanic—A person of Mexican, Puerto Rican, Cuban, Central or South American, or other 

Spanish culture or origin, regardless of race.

Non-Hispanic White—A person having origins in any of the original peoples of Europe, North 

Africa, or the Middle East.

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